Industry increasingly depends upon highly automated data acquisition and control systems to ensure that industrial processes are run efficiently, safely and reliably while lowering their overall production costs. Data acquisition begins when a number of sensors measure aspects of an industrial process and periodically report their measurements back to a data collection and control system. Such measurements come in a wide variety of forms. By way of example, the measurements produced by a sensor/recorder include: a temperature, a pressure, a pH, a mass/volume flow of material, a tallied inventory of packages waiting in a shipping line, or a photograph of a room in a factory. More particularly, aspects of the present invention relate to systems and methods for using tools that are designed to model and simulate the process in order to optimize the process. The use of these modeling and simulation functions allows a user to capture the economic benefit from industrial processes such as refinery, chemical or petrochemical plant operations. Such models make use of known and acquired data from a process to accurately simulate the behavior of the process.
Typical industrial processes are extremely complex and receive substantially greater volumes of information than any human could possibly digest in its raw form. By way of example, it is not unheard of to have thousands of sensors and control elements (e.g., valve actuators) monitoring/controlling aspects of a multi-stage process within an industrial plant. These sensors are of varied type and report on varied characteristics of the process. Their outputs are similarly varied in the meaning of their measurements, in the amount of data sent for each measurement, and in the frequency of their measurements. As regards the latter, for accuracy and to enable quick response, some of these sensors/control elements take one or more measurements every second. Multiplying a single sensor/control element by thousands of sensors/control elements (a typical industrial control environment) results in an overwhelming volume of data flowing into the manufacturing information and process control system. Sophisticated data management and process visualization techniques have been developed to handle the large volumes of data generated by such system.
Due to this complexity, it is a difficult but vital task to ensure that the process is running efficiently. Although modeling the process for the purpose of simulating it is known, the challenges involved in creating and using a simulation model of a process include ensuring the accuracy of the model and responsive performance of the simulation while running. Simulation of a process requires solving large systems of equations, which can be extremely time-consuming and processing intensive.